Time Series Analysis

Accuracy

Extremely accurate computational methods are employed throughout. High marks are earned on all National Institute of Standards and Technology test problems, including:

Descriptive statistics

Analysis of variance

Linear regression

Nonlinear least squares

Post Estimation

Extensive tools for post estimation enable manipulation of model results along with other statistics and procedures.

Data Management

Data management tools are provided for input of data or internal generation with the random number generators, including:

Data transformations

Sampling and bootstrapping

Bootstrap cross section observations or panel groups

Weighted data

Random number generation

Cluster sampling and stratification

Multiple Imputation

Multiple Imputation is used to generate proxies for missing values in order to use information from the model and within the sample to increase the precision of estimators. Missing values for continuous, binary, count, Likert, fractional and multinomial data may be generated. Results from multiple samples are generated and averaged to produce the final results.

What's New in LIMDEP 10?

Version 10 contains major new extensions to the program for estimation and analysis of econometric models and a long list of new models and features.

Model Estimation, Analysis and Simulation

Interactions and Nonlinearities: Models that contain interactions, products, powers and logs of variables are now specified explicitly. The basic command structure is fully integrated throughout the entire program, not just layered on top of a few models. Every specification of every model can use this structure. This will provide a significant convenience in the specification of models. But, the major benefit of this explicit format comes in terms of how it enables you to obtain partial effects and simulations for your models.

Partial Effects: Partial effects, using the sample averages of the effects or calculations at the means of the data, can be computed automatically for any variable in any model regardless of how intricate. Effects can be simulated for specified values or ranges of variables, and tabulated or plotted with confidence intervals. The lack of appropriate calculations of partial effects for models that contain interactions and nonlinearities has been recognized as a major shortcoming of software and of many published analyses. LIMDEP's new PARTIAL EFFECTS command solves this problem. This feature can be used with all models fit by LIMDEP, or with a function of your own that need not be part of any built in model in the program. This feature will change the way you analyze nonlinear models.

Model Simulation and Oaxaca Decomposition: The partial effects feature can also be used to simulate the prediction function (usually the conditional mean) for any model fit by LIMDEP, or any model or equation that you wish to specify in the SIMULATE command. Simulations can involve scenarios, such as tracing the sample average prediction of a probability or a count of outcomes as a function of age. The simulation feature is also useful for computing Oaxaca decompositions for subgroups of the sample. Like PARTIAL EFFECTS and SIMULATE, DECOMPOSE is used with all models built into LIMDEP, or with a function or model that you specify yourself.

Multiple Imputation: The technique of multiple imputation for handling missing data has been gaining popularity. LIMDEP's new implementation of this technique is woven into the entire program, not just a few specific models. Any estimator, even your own created with MAXIMIZE, or any other computation involving data that produces a coefficient vector and a sampling covariance matrix, can be based on multiple imputed data sets. And, we have built this technique to bypass the need to create multiple data sets - traditionally, the need to replicate the full data set has hobbled this method. LIMDEP's implementation of multiple imputation uses only the existing data set. The results are fully replicable as well. (You can create and save the imputed data sets if you wish.)

Extensions of Estimation and Analysis Methods

Streamlined output with additional test and diagnostic statistics

Restrictions and hypothesis tests in all models

Simpler natural format for model specification

Single step estimation for testing multiple hypotheses

Numerous new features for user written iterative and looping procedures

New Wald features for computing standard errors

Sample average functions as an alternative to computing functions at the means of the data

Variances using the delta method account for the averaging procedure

Multiple new functions for matrix algebra program

Expanded graphics capabilities

New additions for kernel density estimation, including plotting multiple KDEs in the same figure

Contour plots

Enhanced tools for creating and labeling graphs

Robust covariance estimators for linear models

Jackknife and bootstrap estimators for standard errors and confidence intervals and for large sample behavior of test statistics

New automated tools for specification tests and calculations such as technical efficiencies

MAXIMIZE/MINIMIZE with random parameters

Numerical Analysis

FUNCTION to plot and simulate any specified function

SOLVE to locate the solutions to f(x)=0

Program Limits at a Glance

We are often asked about LIMDEP's specific internal limits. The following limits are relevant to the most common applications.

Active Data Set

Variables: 900

Observations: 3,000,000+

Total cells in data area: limited by memory

Namelists: 25

Variables in namelist: 100

Command Entry

Characters in one command: 10,000

Characters in a stored procedure: 10,000

Commands in a stored procedure: 100

Stored procedures: 10

Model Size, General & Specific

Number of parameters: 150

Equations for SURE & 3SLS: 30

Equations for WALD, NLSURE, GMM: 50

Panel Data Models

Groups in fixed & random effects:

Linear models: unlimited

Nonlinear models: 100,000

Regressors in fixed & random effects: 150

Periods in linear effects: 1,000

Groups x Regressors: unlimited

Periods in fixed effects (Chamberlain) logit: 100

Matrix & Scalar Algebra

Number of active matrices: 100

Number of active named scalars: 100

Size of a matrix: 50,000 cells

LIMDEP 10 Documentation

The LIMDEP 10 documentation with over 2,500 pages, contains full reference guides for the program, background econometrics, and sample applications. The LIMDEP documentation consists of two guides:

LIMDEP 10 Reference Guide

The LIMDEP 10 Reference Guide provides all instructions for operating the program, including installation, invocation, and most of the basic setup operations that precede model estimation. These operations include reading and transforming data and setting the sample. This manual also describes the optimization procedures, how to use the matrix algebra package and scalar scientific calculator as stand alone tools and as part of LIMDEP programs, what types of results are produced by the program, and some of the common features of the model estimation programs, such as how to do post estimation analysis of model results, including partial effects and simulation. The LIMDEP Reference Guide also includes a complete listing of the program diagnostics.

LIMDEP 10 Econometric Modeling Guide

The LIMDEP 10 Econometric Modeling Guide provides the econometric background, LIMDEP commands, and examples with data, commands and results. Topics are arranged by modeling framework, not by program command. There are chapters on

Descriptive statistics

Linear regression

Panel data analysis

Heteroscedasticity

Binary choice models

Models for count data

Censored and truncated data

Survival models

Nonlinear regression

Time series models

Nonlinear optimization

Sample selection models

and many others. Each model fit by the program is fully documented. The full set of formulas for all computations are shown with complete mathematical documentation of the models. Additional chapters in this guide show how to do numerical analysis and how to program your own estimators.